Many contemporary reciprocating engine controls have integral misfire detection systems. With increasingly stringent emissions standards the assurance of accurate and complete misfire detection under all engine and vehicular operating conditions is becoming mandatory.
Commonly, system designers rely on measurement of crankshaft engine angular velocity, and sometimes crankshaft or other forms of, engine acceleration, both dependent largely on engine torque produced during a firing process, to determine misfiring of a particular engine cylinder. Typically, misfires are predicted by various signature analysis, and/or spectral analysis, methods that analyze the velocity or acceleration information measured.
Some contemporary engine misfire detection systems use position encoders affixed to an engine's crankshaft to determine rotary position of an engine. From this rotary position measurement, an engine speed and/or absolute position information can be derived. With this information the engine can be controlled in accordance with its mission, and combustion misfires can be detected through behavioral signature analysis as noted above. In operation such position encoders are subject to measurement inaccuracies as the engine rotates. These inaccuracies can lead to erroneous indications of true (physical) engine rotary position as the position encoder rotates. Moreover, because of crankshaft dynamics including: crankshaft torsional vibrations, inertial torque due to reciprocating masses, piston mass imbalance, and other mechanically induced vibrations on the engine's crankshaft, the engine rotary position data can effect the accuracy of misfire detection particularly at high engine speeds where these behaviors can largely swamp out any signatory behavior of a misfire event.
Some prior art misfire detection systems minimize position encoder errors by learning their behavior when the engine is not fueled, then subtract-out the learned behavior when the engine is fueled. The engine is defueled to prevent learning of misfire behavior--in case a misfire occurs during the learning process. If a misfire was learned during the learning process, then it would be subtracted out of the behavior and thus any misfires may not be reliably detected. Besides requiring off-line calibration, these schemes do not correct for mechanically induced vibrations on the engine's crankshaft in a fueled condition, nor do they account for changing engine operation with aging.
Another problem with high accuracy prior art misfire determination systems is data rate, or the rate at which the engine's positional data must be analyzed for misfire, and the impact on computational resources to support this high accuracy. In a typical prior art misfire detection system, the system's main microcontroller, or other hardwired circuit, is often charged with analyzing misfire behavior. As misfire detection over all operating conditions becomes required, a greater burden is shared by the main microcontroller in completing this task. This becomes particularly difficult at high engine speeds because the main microcontroller must forfeit much of its resources to service the misfire detection strategy.
What is needed is an improved signal processing approach for misfire detection, particularly one that is less resource intensive and more accurate, particularly at high engine speeds, and adaptive to changing engine conditions over the life of the engine.